Reconstructing Perceptive Images from Brain Activity by Shape-Semantic GAN
This addresses the challenge of brain-reading technology for neuroscience applications, though it appears incremental as it builds on existing hierarchical visual encoding theories.
The paper tackles the problem of reconstructing visual images from fMRI brain activity recordings by proposing a Shape-Semantic GAN that decodes shape and semantic representations separately from different visual cortex regions and merges them. The method improves reconstruction similarity and image quality, achieving state-of-the-art performance.
Reconstructing seeing images from fMRI recordings is an absorbing research area in neuroscience and provides a potential brain-reading technology. The challenge lies in that visual encoding in brain is highly complex and not fully revealed. Inspired by the theory that visual features are hierarchically represented in cortex, we propose to break the complex visual signals into multi-level components and decode each component separately. Specifically, we decode shape and semantic representations from the lower and higher visual cortex respectively, and merge the shape and semantic information to images by a generative adversarial network (Shape-Semantic GAN). This 'divide and conquer' strategy captures visual information more accurately. Experiments demonstrate that Shape-Semantic GAN improves the reconstruction similarity and image quality, and achieves the state-of-the-art image reconstruction performance.